Explanation-based neural network learning (EBNN) has recently been introduced
as a method for reducing the amount of training data required for reliable
generalization, by relying instead on approximate, previously learned
knowledge. We present first experiments applying EBNN to the problem of
learning object recognition for a mobile robot. In these experiments, a
mobile robot traveling down a hallway corridor learns to recognize distant
doors based on color camera images and sonar sensations. The previously
learned knowledge corresponds to a neural network that recognizes nearby
doors, and a second network that predicts the state of the world after
travelling forward in the corridor. Experimental results show that EBNN is
able to use this approximate prior knowledge to significantly reduce the
number of training examples required to learn to recognize distant doors. We
also present results of experiments in which networks learned by EBNN (e.g.,
``there is a door 2 meters ahead'') are then used as background knowledge for
learning subsequent functions (e.g., ``there is a door 3 meters ahead'').